Libraries

github link: https://github.com/becca-belmonte/H99_RNAseq

Load data

Functions

Analysis

First, we can see that the samples primarily differ in expression due to being control or H99 samples. We can also see the top differentially expressed genes, both those higher in control and those higher in H99 mutants.

#rownames(col_data) %in% colnames(raw_counts)

matrix_raw_counts <- as.matrix(raw_counts)

dds <- DESeqDataSetFromMatrix(countData = matrix_raw_counts, 
                                   colData = col_data,
                                   design = ~ Group)

dds <- estimateSizeFactors(dds)

vsdB = varianceStabilizingTransformation(dds)
plotPCA(vsdB,intgroup = c("Group"))

ddsTC <- DESeq(dds)
resTC <- results(ddsTC)

resTC$gene <- rownames(resTC)
tab_resTC <- as.data.frame(resTC)
tab_resTC$gene <- rownames(tab_resTC)

tab_resTC <- cbind(tab_resTC, Gene_ID_Flybase[match(tab_resTC$gene, Gene_ID_Flybase$FBgn_ID), "Gene_name"])
colnames(tab_resTC)[ncol(tab_resTC)] = "Gene_name"

tab_resTC <- cbind(tab_resTC, Gene_ID_Flybase[match(tab_resTC$gene, Gene_ID_Flybase$FBgn_ID), "CG_ID"])
colnames(tab_resTC)[ncol(tab_resTC)] = "CG_ID"

tab_resTC <- tab_resTC %>% 
  mutate(Gene_name = coalesce(Gene_name, gene))

tab_top <- tab_resTC %>% 
  filter(abs(log2FoldChange) > 1 & padj < 0.05) %>% 
  arrange(padj)

tab_top_down <- tab_top %>% 
  filter(log2FoldChange < 0)

kable(head(tab_top, 10)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>% 
  scroll_box(width = "100%")
baseMean log2FoldChange lfcSE stat pvalue padj gene Gene_name CG_ID
FBgn0085466 43263.1941 4.824477 0.2282038 21.14109 0 0 FBgn0085466 FBgn0085466 NA
FBgn0020493 4746.8218 -5.549997 0.2652973 -20.91992 0 0 FBgn0020493 Dad CG5201
FBgn0037930 6747.1360 3.919628 0.2287028 17.13852 0 0 FBgn0037930 CG14715 CG14715
FBgn0052085 1674.8032 8.845837 0.5171809 17.10395 0 0 FBgn0052085 FBgn0052085 NA
FBgn0023129 25753.8140 2.400866 0.1460641 16.43707 0 0 FBgn0023129 aay CG3705
FBgn0025678 53362.0963 -3.282450 0.2067646 -15.87530 0 0 FBgn0025678 CaBP1 CG5809
FBgn0037222 22074.0559 -2.704999 0.1858704 -14.55314 0 0 FBgn0037222 CG14642 CG14642
FBgn0039099 15792.9504 -2.203793 0.1717471 -12.83161 0 0 FBgn0039099 GILT2 CG10157
FBgn0051813 3122.6405 3.710390 0.2991312 12.40389 0 0 FBgn0051813 nur CG31813
FBgn0267472 396.0726 -5.570781 0.4826268 -11.54263 0 0 FBgn0267472 asRNA:CR45822 CR45822
#write.csv(tab_top, file = "Results/tab_top.csv")

GSEA

To get a better idea of what these genes are doing, I did a gene set enrichment analysis. First I looked at the reactome and unfortunately, none of them are significant with the adjusted p-value. But, we do see that Golgi Associated Vesicle Biogenesis is particularly enriched in H99 mutants when compared to the control. I then looked at KEGG pathways and saw that the phagosome pathway was enriched in control samples when compared to H99 mutants.

colors_vol <- c(rev(viridis(2)), "darkgrey")

ranks <- tab_resTC$log2FoldChange[!is.na(tab_resTC$log2FoldChange)]
names(ranks) <- tab_resTC$CG_ID[!is.na(tab_resTC$log2FoldChange)]
#head(ranks)
#barplot(sort(ranks, decreasing = T))

fgseaRes <- fgsea(t_Drosophila_REACTOME, ranks, minSize = 15, maxSize = 500)
head(fgseaRes[order(padj, -abs(NES)), ], n=10)
##                                                                 pathway
##  1:                                 Golgi Associated Vesicle Biogenesis
##  2:                                      Phosphorylation of PER and TIM
##  3:                          Signaling by Non-Receptor Tyrosine Kinases
##  4:                                                   Signaling by PTK6
##  5:                                                 Intra-Golgi traffic
##  6:                                   RNA Polymerase II Promoter Escape
##  7:                          RNA Polymerase II Transcription Initiation
##  8:   RNA Polymerase II Transcription Initiation And Promoter Clearance
##  9: RNA Polymerase II Transcription Pre-Initiation And Promoter Opening
## 10:                                           MAP2K and MAPK activation
##            pval padj   log2err         ES       NES size
##  1: 0.002945886    1 0.4317077  0.7107300  1.724223   27
##  2: 0.020256094    1 0.3524879  0.7349901  1.554007   15
##  3: 0.028444229    1 0.3524879  0.6875806  1.527877   18
##  4: 0.028444229    1 0.3524879  0.6875806  1.527877   18
##  5: 0.022954825    1 0.3524879  0.6294295  1.526989   27
##  6: 0.021969374    1 0.3524879 -0.6054070 -1.508175   38
##  7: 0.021969374    1 0.3524879 -0.6054070 -1.508175   38
##  8: 0.021969374    1 0.3524879 -0.6054070 -1.508175   38
##  9: 0.021969374    1 0.3524879 -0.6054070 -1.508175   38
## 10: 0.070981211    1 0.2450418  0.6082707  1.448003   24
##                                          leadingEdge
##  1:                            CG32683,CG4349,CG5711
##  2:                                    CG3772,CG2048
##  3:                     CG3927,CG10384,CG3875,CG4021
##  4:                     CG3927,CG10384,CG3875,CG4021
##  5:       CG10592,CG5150,CG3292,CG7980,CG8105,CG8147
##  6:                     CG14718,CG7562,CG9879,CG3180
##  7:                     CG14718,CG7562,CG9879,CG3180
##  8:                     CG14718,CG7562,CG9879,CG3180
##  9:                     CG14718,CG7562,CG9879,CG3180
## 10: CG32683,CG5711,CG31832,CG7524,CG8642,CG10359,...
plotEnrichment(t_Drosophila_REACTOME[["Golgi Associated Vesicle Biogenesis"]], ranks) +
  ggtitle("Golgi Associated Vesicle Biogenesis")

fgseaRes <- fgseaRes %>% 
  mutate(color = factor(case_when(NES >= 1 & pval < 0.05 ~ "blue", 
                                  NES <= -1 & pval < 0.05 ~ "red", 
                                  NES %in% c(-1:1) ~ "grey")))

ggplot(fgseaRes, aes(x = NES, y = -log10(pval))) +
  geom_point(aes(color = color))+
  geom_text_repel(data = (fgseaRes %>% filter(pval < 0.025)),aes(label = pathway)) +
  ggtitle("Enriched pathways without regulators of apoptosis") +
  theme_light()+
  scale_colour_manual(limits=c("blue","red", "black"),
                      values = colors_vol,
                      labels=c("Enriched in H99","Enriched in control","ns")) +
  labs(color = "")

fgseaRes_KEGG <- fgsea(t_Drosophila_KEGG, ranks, minSize = 15, maxSize = 500)
head(fgseaRes_KEGG[order(padj, -abs(NES)), ], n=10)
##                                     pathway       pval padj   log2err
##  1:                               Phagosome 0.04447955    1 0.3217759
##  2: Biosynthesis of unsaturated fatty acids 0.10052910    1 0.1864326
##  3:                    Galactose metabolism 0.14010508    1 0.1552420
##  4:             Basal transcription factors 0.11981567    1 0.1957890
##  5:             Insect hormone biosynthesis 0.14712644    1 0.1752040
##  6:              Apoptosis_multiple species 0.17040359    1 0.1596467
##  7:                        ABC transporters 0.19964974    1 0.1275053
##  8:      Cysteine and methionine metabolism 0.19237435    1 0.1294429
##  9:                   N Glycan biosynthesis 0.17667845    1 0.1372508
## 10:            Longevity regulating pathway 0.16067146    1 0.1709323
##             ES       NES size
##  1: -0.5395880 -1.446518   57
##  2: -0.6490166 -1.365043   16
##  3: -0.6023040 -1.314794   19
##  4:  0.5035614  1.293475   32
##  5:  0.5763314  1.264763   16
##  6:  0.5861498  1.264691   15
##  7: -0.5730456 -1.250924   19
##  8: -0.5299475 -1.243260   27
##  9: -0.5038413 -1.228812   34
## 10:  0.4696273  1.223888   38
##                                            leadingEdge
##  1:      CG8310,CG1076,CG9906,CG1924,CG7794,CG7678,...
##  2:                     CG15531,CG9747,CG18609,CG10849
##  3:  CG8695,CG14934,CG12766,CG32444,CG10638,CG5165,...
##  4:                      CG15632,CG11639,CG5444,CG1276
##  5: CG13478,CG40486,CG31075,CG40485,CG10594,CG6578,...
##  6:         CG8238,CG6531,CG8091,CG7788,CG6829,CG14902
##  7:               CG4562,CG8799,CG8908,CG10181,CG31792
##  8:                             CG11899,CG6287,CG31115
##  9:                                     CG4871,CG17173
## 10:    CG14173,CG14167,CG7756,CG7978,CG5948,CG1506,...
plotEnrichment(t_Drosophila_KEGG[["Phagosome"]], ranks) +
  ggtitle("Phagosome")

fgseaRes_KEGG <- fgseaRes_KEGG %>% 
  mutate(color = factor(case_when(NES >= 1 & pval < 0.05 ~ "blue", 
                                  NES <= -1 & pval < 0.05 ~ "red", 
                                  NES %in% c(-1:1) ~ "grey")))

ggplot(fgseaRes_KEGG, aes(x = NES, y = -log10(pval))) +
  geom_point(aes(color = color))+
  geom_text_repel(data = (fgseaRes_KEGG %>% filter(pval < 0.1)),aes(label = pathway)) +
  ggtitle("Enriched KEGG pathways without regulators of apoptosis") +
  theme_light()+
  scale_colour_manual(limits=c("blue","red", "black"),
                      values = colors_vol,
                      labels=c("Enriched in H99","Enriched in control","ns")) +
  labs(color = "")

phago_genes <- tab_resTC %>% 
  filter(CG_ID %in% t_Drosophila_KEGG[["Phagosome"]])

top_phago_genes <- c("FBgn0264077", "FBgn0003884", "FBgn0005671", "FBgn0040377", "FBgn0261797")

Raw counts

I wanted to get a better idea of which genes were lost in H99 mutants, and particularly those in the phagosome pathway.

best12 <- head(tab_top_down,12)
best12_genes <- best12$gene

col_data <- col_data %>% 
  arrange(ID)

normalized <- normalized_counts %>% 
  mutate(gene = rownames(normalized_counts)) %>% 
  filter(gene %in% best12_genes) %>% 
  select(-gene)
normalized <- as.data.frame(t(normalized))
normalized <- normalized %>% 
  mutate(group = rownames(normalized)) %>% 
  arrange(group) %>% 
  cbind(col_data) %>% 
  pivot_longer(best12_genes, names_to = "gene", values_to = "counts")
normalized$Rep <- as.factor(normalized$Rep)

normalized <- cbind(normalized, Gene_ID_Flybase[match(normalized$gene, Gene_ID_Flybase$FBgn_ID), "Gene_name"])
colnames(normalized)[ncol(normalized)] = "Gene_name"
normalized <- normalized %>% 
  mutate(Gene_name = coalesce(Gene_name, gene))

(rawcounts <- ggplot(normalized, aes(x = Group, y = counts)) +
  geom_point(aes(color = Group), position = position_dodge(width = 0.75)) +
  scale_color_manual(values = (viridis(2))) +
  facet_wrap(~Gene_name, scale = "free_y") +
  theme_classic() +
  xlab("Genotype") +
  ylab("Normalized counts") +
  ggtitle("Top differentially expressed genes without apoptosis"))

col_data <- col_data %>% 
  arrange(ID)

normalized <- normalized_counts %>% 
  mutate(gene = rownames(normalized_counts)) %>% 
  filter(gene %in% top_phago_genes) %>% 
  select(-gene)
normalized <- as.data.frame(t(normalized))
normalized <- normalized %>% 
  mutate(group = rownames(normalized)) %>% 
  arrange(group) %>% 
  cbind(col_data) %>% 
  pivot_longer(top_phago_genes, names_to = "gene", values_to = "counts")
normalized$Rep <- as.factor(normalized$Rep)

normalized <- cbind(normalized, Gene_ID_Flybase[match(normalized$gene, Gene_ID_Flybase$FBgn_ID), "Gene_name"])
colnames(normalized)[ncol(normalized)] = "Gene_name"
normalized <- normalized %>% 
  mutate(Gene_name = coalesce(Gene_name, gene))

(rawcounts <- ggplot(normalized, aes(x = Group, y = counts)) +
  geom_point(aes(color = Group), position = position_dodge(width = 0.75)) +
  scale_color_manual(values = (viridis(2))) +
  facet_wrap(~Gene_name, scale = "free_y") +
  theme_classic() +
  xlab("Genotype") +
  ylab("Normalized counts") +
  ggtitle("Top differentially expressed phagosome-associated genes without apoptosis"))

Heatmap

Volcano plot

With the volcano plot, we can see that Dad and CaBP1 are downregulated in H99 mutants, while FBgn0085466 (which is CG34437) and CG14715 are upregulated in H99 mutants.

colors_vol <- c(rev(viridis(2)), "darkgrey")
tab_resTC <- tab_resTC %>% 
  mutate(color = factor(case_when(log2FoldChange >= 2 & padj < 0.05 ~ "up", 
                                  log2FoldChange <= -2 & padj < 0.05 ~ "down", 
                                  log2FoldChange %in% c(-2:2) ~ "ns"))) %>% 
  mutate(significance = case_when(log2FoldChange >= 2 ~ "yes", log2FoldChange <= -2 ~ "yes"))
significant <- subset(tab_resTC, significance=="yes")
sig_genes <- significant$gene

tab_resTC$color <- as.character(tab_resTC$color)
tab_resTC$color <- replace_na(tab_resTC$color,"ns")

tab_resTC <- tab_resTC %>% 
  mutate(Gene_name = coalesce(Gene_name, gene))


(carcass <- ggplot(tab_resTC, aes(x = log2FoldChange, y = -log10(pvalue), label = Gene_name)) +
  geom_point(aes(color = color), alpha = 0.5)+
  geom_text_repel(data = tab_resTC %>% filter(log2FoldChange < -2 | log2FoldChange > 2.5) %>% filter(pvalue <10^-5), aes(label = Gene_name)) +
  geom_vline(xintercept=2,color="darkgrey", linetype = "dotted")+
  geom_vline(xintercept=-2,color="darkgrey", linetype = "dotted")+
  geom_hline(yintercept=-log10(0.05),color="darkgrey",linetype="dashed") +
  theme_bw() +
  scale_colour_manual(limits=c("up","down", "ns"),
                      values = colors_vol,
                      labels=c("Upregulated in H99","Upregulated in control","ns")) +
  labs(color = "") +
  ggtitle("Differential expression in control and H99 mutants"))

ggplotly(carcass)

GO

The first table shows GO terms that are enriched in upregulated genes, so those that are higher in H99 mutants than control. The second table shows GO terms enriched in downregulated genes, those that are lower in H99 mutants.

mart <- useDataset(dataset = "dmelanogaster_gene_ensembl",
                   mart = useMart("ENSEMBL_MART_ENSEMBL"))
resultTable <- getBM(attributes = c("flybase_gene_id", "external_gene_name","go_id", "name_1006", "definition_1006"),
                     mart = mart)
resultTable <- resultTable[resultTable$go_id != '',]
geneID2GO <- by(resultTable$go_id,
                resultTable$external_gene_name,
                function(x) as.character(x))

datRef <- tab_resTC$Gene_name #full set of genes in your analysis (ie. rownames of your summarised experiment input into DEseq2)
tab_top_up <- tab_top %>% 
  filter(log2FoldChange > 0)

theGenes <- tab_top_up$Gene_name#set of flybase IDs that you want to query

geneNames <- tab_resTC$Gene_name
myInterestingGenes <- tab_top_up$Gene_name

geneList <- tab_resTC %>% 
  filter(Gene_name %in% myInterestingGenes) %>% 
  na.omit(geneList)
geneList <- geneList %>% 
  arrange(pvalue)
geneList_pvalue <- geneList[,5]
geneList_name <- as.character(geneList$Gene_name)
names(geneList_pvalue) <- geneList_name
geneList <- geneList_pvalue

#write.csv(geneList_F_UC_8,"geneList_F_UC_8.csv",col.names=F,row.names=T)


all_genes <- sort(unique(as.character(resultTable$external_gene_name)))
int_genes <- factor(as.integer(all_genes %in% geneList_name))
names(int_genes) = all_genes

GOdata <- new("topGOdata", ontology = "BP", allGenes = int_genes, 
              annot = annFUN.gene2GO, gene2GO = geneID2GO)

GO_results_classic <- runTest(GOdata, algorithm = "classic", statistic = "Fisher")
GO_results_elim <- runTest(GOdata, algorithm = "elim", statistic = "Fisher")
GO_results_tab_up <- GenTable(object = GOdata, classic = GO_results_classic, elim = GO_results_elim, orderBy = "elim", ranksOf = "classic", topNodes = 50)

kable(GO_results_tab_up) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>% scroll_box(width = "100%")
GO.ID Term Annotated Significant Expected Rank in classic classic elim
GO:0098754 detoxification 87 4 0.55 1 0.0023 0.0023
GO:0043153 entrainment of circadian clock by photop… 12 2 0.08 2 0.0025 0.0025
GO:0009785 blue light signaling pathway 1 1 0.01 4 0.0064 0.0064
GO:0010835 regulation of protein ADP-ribosylation 1 1 0.01 5 0.0064 0.0064
GO:0014853 regulation of excitatory postsynaptic me… 1 1 0.01 6 0.0064 0.0064
GO:0018312 peptidyl-serine ADP-ribosylation 1 1 0.01 7 0.0064 0.0064
GO:0030216 keratinocyte differentiation 1 1 0.01 8 0.0064 0.0064
GO:0032481 positive regulation of type I interferon… 1 1 0.01 9 0.0064 0.0064
GO:0050843 S-adenosylmethionine catabolic process 1 1 0.01 10 0.0064 0.0064
GO:0050980 detection of light stimulus involved in … 1 1 0.01 11 0.0064 0.0064
GO:0071465 cellular response to desiccation 1 1 0.01 12 0.0064 0.0064
GO:1901054 sarcosine biosynthetic process 1 1 0.01 13 0.0064 0.0064
GO:0030001 metal ion transport 285 6 1.82 25 0.0096 0.0096
GO:0007610 behavior 588 9 3.75 26 0.0122 0.0122
GO:0006111 regulation of gluconeogenesis 2 1 0.01 27 0.0127 0.0127
GO:0009588 UV-A, blue light phototransduction 2 1 0.01 28 0.0127 0.0127
GO:0009639 response to red or far red light 2 1 0.01 29 0.0127 0.0127
GO:0010114 response to red light 2 1 0.01 30 0.0127 0.0127
GO:0015770 sucrose transport 2 1 0.01 31 0.0127 0.0127
GO:0048150 behavioral response to ether 2 1 0.01 32 0.0127 0.0127
GO:0061965 positive regulation of entry into reprod… 2 1 0.01 33 0.0127 0.0127
GO:0071000 response to magnetism 2 1 0.01 34 0.0127 0.0127
GO:0071929 alpha-tubulin acetylation 2 1 0.01 35 0.0127 0.0127
GO:0120176 positive regulation of torso signaling p… 2 1 0.01 36 0.0127 0.0127
GO:0001752 compound eye photoreceptor fate commitme… 77 3 0.49 39 0.0130 0.0130
GO:0042706 eye photoreceptor cell fate commitment 77 3 0.49 40 0.0130 0.0130
GO:0007218 neuropeptide signaling pathway 78 3 0.50 41 0.0135 0.0135
GO:0048878 chemical homeostasis 314 6 2.00 42 0.0149 0.0149
GO:0042592 homeostatic process 510 8 3.25 43 0.0156 0.0156
GO:0046552 photoreceptor cell fate commitment 84 3 0.54 45 0.0164 0.0164
GO:0098659 inorganic cation import across plasma me… 32 2 0.20 46 0.0176 0.0176
GO:0099587 inorganic ion import across plasma membr… 32 2 0.20 47 0.0176 0.0176
GO:0030534 adult behavior 157 4 1.00 48 0.0179 0.0179
GO:0006564 L-serine biosynthetic process 3 1 0.02 50 0.0190 0.0190
GO:0008302 female germline ring canal formation, ac… 3 1 0.02 51 0.0190 0.0190
GO:0030046 parallel actin filament bundle assembly 3 1 0.02 52 0.0190 0.0190
GO:0045472 response to ether 3 1 0.02 53 0.0190 0.0190
GO:0046498 S-adenosylhomocysteine metabolic process 3 1 0.02 54 0.0190 0.0190
GO:0007155 cell adhesion 242 5 1.54 57 0.0190 0.0190
GO:0008543 fibroblast growth factor receptor signal… 37 2 0.24 59 0.0232 0.0232
GO:0043270 positive regulation of ion transport 37 2 0.24 60 0.0232 0.0232
GO:0044344 cellular response to fibroblast growth f… 37 2 0.24 61 0.0232 0.0232
GO:0071774 response to fibroblast growth factor 37 2 0.24 62 0.0232 0.0232
GO:0008340 determination of adult lifespan 172 4 1.10 63 0.0241 0.0241
GO:0000768 syncytium formation by plasma membrane f… 38 2 0.24 64 0.0243 0.0243
GO:0006949 syncytium formation 38 2 0.24 65 0.0243 0.0243
GO:0007520 myoblast fusion 38 2 0.24 66 0.0243 0.0243
GO:0014902 myotube differentiation 38 2 0.24 67 0.0243 0.0243
GO:0140253 cell-cell fusion 38 2 0.24 68 0.0243 0.0243
GO:0048663 neuron fate commitment 99 3 0.63 70 0.0253 0.0253
tab_top_down <- tab_top %>% 
  filter(log2FoldChange < 0)

theGenes <- tab_top_down$Gene_name#set of flybase IDs that you want to query

geneNames <- tab_resTC$Gene_name
myInterestingGenes <- tab_top_down$Gene_name

geneList <- tab_resTC %>% 
  filter(Gene_name %in% myInterestingGenes) %>% 
  na.omit(geneList)
geneList <- geneList %>% 
  arrange(pvalue)
geneList_pvalue <- geneList[,5]
geneList_name <- as.character(geneList$Gene_name)
names(geneList_pvalue) <- geneList_name
geneList <- geneList_pvalue

#write.csv(geneList_F_UC_8,"geneList_F_UC_8.csv",col.names=F,row.names=T)


all_genes <- sort(unique(as.character(resultTable$external_gene_name)))
int_genes <- factor(as.integer(all_genes %in% geneList_name))
names(int_genes) = all_genes

GOdata <- new("topGOdata", ontology = "BP", allGenes = int_genes, 
              annot = annFUN.gene2GO, gene2GO = geneID2GO)

GO_results_classic <- runTest(GOdata, algorithm = "classic", statistic = "Fisher")
GO_results_elim <- runTest(GOdata, algorithm = "elim", statistic = "Fisher")
GO_results_tab_down <- GenTable(object = GOdata, classic = GO_results_classic, elim = GO_results_elim, orderBy = "elim", ranksOf = "classic", topNodes = 50)

kable(GO_results_tab_down) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>% scroll_box(width = "100%")
GO.ID Term Annotated Significant Expected Rank in classic classic elim
GO:0007094 mitotic spindle assembly checkpoint sign… 15 3 0.10 1 0.00013 0.00013
GO:0007443 Malpighian tubule morphogenesis 44 4 0.30 13 0.00022 0.00022
GO:0035002 liquid clearance, open tracheal system 24 3 0.16 19 0.00055 0.00055
GO:0048133 male germ-line stem cell asymmetric divi… 8 2 0.05 27 0.00125 0.00125
GO:0008362 chitin-based embryonic cuticle biosynthe… 17 2 0.12 44 0.00582 0.00582
GO:0010991 negative regulation of SMAD protein comp… 1 1 0.01 46 0.00680 0.00680
GO:0032499 detection of peptidoglycan 1 1 0.01 47 0.00680 0.00680
GO:0033316 meiotic spindle assembly checkpoint sign… 1 1 0.01 48 0.00680 0.00680
GO:0042940 D-amino acid transport 1 1 0.01 49 0.00680 0.00680
GO:0043060 meiotic metaphase I plate congression 1 1 0.01 50 0.00680 0.00680
GO:0060394 negative regulation of pathway-restricte… 1 1 0.01 51 0.00680 0.00680
GO:0061734 parkin-mediated stimulation of mitophagy… 1 1 0.01 52 0.00680 0.00680
GO:1901526 positive regulation of mitophagy 1 1 0.01 53 0.00680 0.00680
GO:1901693 negative regulation of compound eye reti… 1 1 0.01 54 0.00680 0.00680
GO:1904068 G protein-coupled receptor signaling pat… 1 1 0.01 55 0.00680 0.00680
GO:1905342 positive regulation of protein localizat… 1 1 0.01 56 0.00680 0.00680
GO:0006030 chitin metabolic process 21 2 0.14 68 0.00883 0.00883
GO:0035214 eye-antennal disc development 64 3 0.44 69 0.00938 0.00938
GO:0060438 trachea development 23 2 0.16 71 0.01054 0.01054
GO:0051240 positive regulation of multicellular org… 197 5 1.34 73 0.01094 0.01094
GO:0031644 regulation of nervous system process 24 2 0.16 74 0.01145 0.01145
GO:0044057 regulation of system process 69 3 0.47 76 0.01152 0.01152
GO:0042753 positive regulation of circadian rhythm 25 2 0.17 77 0.01239 0.01239
GO:0061983 meiosis II cell cycle process 26 2 0.18 79 0.01337 0.01337
GO:0006037 cell wall chitin metabolic process 2 1 0.01 81 0.01356 0.01356
GO:0006038 cell wall chitin biosynthetic process 2 1 0.01 82 0.01356 0.01356
GO:0007066 female meiosis sister chromatid cohesion 2 1 0.01 83 0.01356 0.01356
GO:0009305 protein biotinylation 2 1 0.01 84 0.01356 0.01356
GO:0010460 positive regulation of heart rate 2 1 0.01 85 0.01356 0.01356
GO:0018054 peptidyl-lysine biotinylation 2 1 0.01 86 0.01356 0.01356
GO:0045760 positive regulation of action potential 2 1 0.01 87 0.01356 0.01356
GO:0048075 positive regulation of eye pigmentation 2 1 0.01 88 0.01356 0.01356
GO:0048078 positive regulation of compound eye pigm… 2 1 0.01 89 0.01356 0.01356
GO:0051971 positive regulation of transmission of n… 2 1 0.01 90 0.01356 0.01356
GO:0060279 positive regulation of ovulation 2 1 0.01 91 0.01356 0.01356
GO:0071110 histone biotinylation 2 1 0.01 92 0.01356 0.01356
GO:0110123 regulation of myotube cell migration 2 1 0.01 93 0.01356 0.01356
GO:0110125 negative regulation of myotube cell migr… 2 1 0.01 94 0.01356 0.01356
GO:1904061 positive regulation of locomotor rhythm 2 1 0.01 95 0.01356 0.01356
GO:1904457 positive regulation of neuronal action p… 2 1 0.01 96 0.01356 0.01356
GO:1905050 positive regulation of metallopeptidase … 2 1 0.01 97 0.01356 0.01356
GO:0007448 anterior/posterior pattern specification… 27 2 0.18 112 0.01437 0.01437
GO:0015807 L-amino acid transport 28 2 0.19 113 0.01541 0.01541
GO:1902475 L-alpha-amino acid transmembrane transpo… 28 2 0.19 114 0.01541 0.01541
GO:0043171 peptide catabolic process 29 2 0.20 115 0.01648 0.01648
GO:0000705 achiasmate meiosis I 3 1 0.02 118 0.02027 0.02027
GO:0008614 pyridoxine metabolic process 3 1 0.02 119 0.02027 0.02027
GO:0008615 pyridoxine biosynthetic process 3 1 0.02 120 0.02027 0.02027
GO:0043567 regulation of insulin-like growth factor… 3 1 0.02 121 0.02027 0.02027
GO:0045678 positive regulation of R7 cell different… 3 1 0.02 122 0.02027 0.02027